How Much Can I Trust You? - Quantifying Uncertainties in Explaining Neural Networks. - Kirill Bykov; Marina M.-C. Höhne; Klaus-Robert Müller; Shinichi Nakajima; Marius Kloft (CoRR, 2020)

Explaining Bayesian Neural Networks. - Kirill Bykov; Marina M.-C. Höhne; Adelaida Creosteanu; Klaus-Robert Müller; Frederick Klauschen; Shinichi Nakajima; Marius Kloft (CoRR, 2021)

This looks more like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation. - Srishti Gautam; Marina M.-C. Höhne; Stine Hansen; Robert Jenssen; Michael Kampffmeyer (CoRR, 2021)

Self-Supervised Learning for 3D Medical Image Analysis using 3D SimCLR and Monte Carlo Dropout. - Yamen Ali; Aiham Taleb; Marina M.-C. Höhne; Christoph Lippert (CoRR, 2021)

Nachvollziehbare Künstliche Intelligenz: Methoden, Chancen und Risiken. - Marina Marie-Claire Höhne (Datenschutz und Datensicherheit, 2021)

NoiseGrad - Enhancing Explanations by Introducing Stochasticity to Model Weights. - Kirill Bykov; Anna Hedström; Shinichi Nakajima; Marina M.-C. Höhne (AAAI, 2022)

Demonstrating the Risk of Imbalanced Datasets in Chest X-Ray Image-Based Diagnostics by Prototypical Relevance Propagation. - Srishti Gautam; Marina M.-C. Höhne; Stine Hansen; Robert Jenssen; Michael Kampffmeyer (ISBI, 2022)

ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model. - Srishti Gautam; Ahcène Boubekki; Stine Hansen; Suaiba Amina Salahuddin; Robert Jenssen; Marina M.-C. Höhne; Michael Kampffmeyer (NeurIPS, 2022)

Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations. - Anna Hedström; Leander Weber; Dilyara Bareeva; Franz Motzkus; Wojciech Samek; Sebastian Lapuschkin; Marina M.-C. Höhne (CoRR, 2022)

Mark My Words: Dangers of Watermarked Images in ImageNet. - Kirill Bykov; Klaus-Robert Müller; Marina M.-C. Höhne (ECAI Workshops (1), 2023)

Kidnapping Deep Learning-based Multirotors using Optimized Flying Adversarial Patches. - Pia Hanfeld; Khaled Wahba; Marina M.-C. Höhne; Michael Bussmann; Wolfgang Hönig (MRS, 2023)

Labeling Neural Representations with Inverse Recognition. - Kirill Bykov; Laura Kopf; Shinichi Nakajima; Marius Kloft; Marina M.-C. Höhne (NeurIPS, 2023)

Finding Spurious Correlations with Function-Semantic Contrast Analysis. - Kirill Bykov; Laura Kopf; Marina M.-C. Höhne (xAI (2), 2023)

Finding the right XAI method - A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science. - Philine Lou Bommer; Marlene Kretschmer; Anna Hedström; Dilyara Bareeva; Marina M.-C. Höhne (CoRR, 2023)

Flying Adversarial Patches: Manipulating the Behavior of Deep Learning-based Autonomous Multirotors. - Pia Hanfeld; Marina M.-C. Höhne; Michael Bussmann; Wolfgang Hönig (CoRR, 2023)

Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond. - Anna Hedström; Leander Weber; Daniel Krakowczyk; Dilyara Bareeva; Franz Motzkus; Wojciech Samek; Sebastian Lapuschkin; Marina M.-C. Höhne (J. Mach. Learn. Res., 2023)

DORA: Exploring Outlier Representations in Deep Neural Networks. - Kirill Bykov; Mayukh Deb; Dennis Grinwald; Klaus-Robert Müller; Marina M.-C. Höhne (Trans. Mach. Learn. Res., 2023)

Visualizing the Diversity of Representations Learned by Bayesian Neural Networks. - Dennis Grinwald; Kirill Bykov; Shinichi Nakajima; Marina M.-C. Höhne (Trans. Mach. Learn. Res., 2023)

The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus. - Anna Hedström; Philine Lou Bommer; Kristoffer Knutsen Wickstrøm; Wojciech Samek; Sebastian Lapuschkin; Marina M.-C. Höhne (Trans. Mach. Learn. Res., 2023)

This looks More Like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation. - Srishti Gautam; Marina M.-C. Höhne; Stine Hansen; Robert Jenssen; Michael Kampffmeyer (Pattern Recognit., 2023)

From Flexibility to Manipulation: The Slippery Slope of XAI Evaluation. - Kristoffer Wickstrøm; Marina M.-C. Höhne; Anna Hedström (ECCV Workshops (21), 2024)

Deep Learning-based UAV-assisted grassland monitoring to facilitate Eco-scheme 5 realization. - Deepak Hanike Basavegowda; Marina M.-C. Höhne; Cornelia Weltzien (GIL, 2024)

Explainable AI in grassland monitoring: Enhancing model performance and domain adaptability. - Shanghua Liu; Anna Hedström; Deepak Hanike Basavegowda; Cornelia Weltzien; Marina M.-C. Höhne (GIL, 2024)

CoSy: Evaluating Textual Explanations of Neurons. - Laura Kopf; Philine Lou Bommer; Anna Hedström; Sebastian Lapuschkin; Marina M.-C. Höhne; Kirill Bykov (NeurIPS, 2024)

A Fresh Look at Sanity Checks for Saliency Maps. - Anna Hedström; Leander Weber; Sebastian Lapuschkin; Marina M.-C. Höhne (xAI (1), 2024)

Model Guidance via Explanations Turns Image Classifiers into Segmentation Models. - Xiaoyan Yu; Jannik Franzen; Wojciech Samek; Marina M.-C. Höhne; Dagmar Kainmueller (xAI (2), 2024)

Manipulating Feature Visualizations with Gradient Slingshots. - Dilyara Bareeva; Marina M.-C. Höhne; Alexander Warnecke; Lukas Pirch; Klaus-Robert Müller; Konrad Rieck; Kirill Bykov (CoRR, 2024)

Sanity Checks Revisited: An Exploration to Repair the Model Parameter Randomisation Test. - Anna Hedström; Leander Weber; Sebastian Lapuschkin; Marina M.-C. Höhne (CoRR, 2024)

Prototypical Self-Explainable Models Without Re-training. - Srishti Gautam; Ahcene Boubekki; Marina M.-C. Höhne; Michael Kampffmeyer (Trans. Mach. Learn. Res., 2024)

Evaluate with the Inverse: Efficient Approximation of Latent Explanation Quality Distribution. - Carlos Eiras-Franco; Anna Hedström; Marina M.-C. Höhne (AAAI, 2025)

Evaluating Interpretable Methods via Geometric Alignment of Functional Distortions. - Anna Hedström; Philine Lou Bommer; Thomas F. Burns; Sebastian Lapuschkin; Wojciech Samek; Marina M.-C. Höhne (Trans. Mach. Learn. Res., 2025)

Deep Learning Meets Teleconnections: Improving S2S Predictions for European Winter Weather. - Philine Lou Bommer; Marlene Kretschmer; Fiona R. Spuler; Kirill Bykov; Marina M.-C. Höhne (CoRR, 2025)

Capturing Polysemanticity with PRISM: A Multi-Concept Feature Description Framework. - Laura Kopf; Nils Feldhus; Kirill Bykov; Philine Lou Bommer; Anna Hedström; Marina M.-C. Höhne; Oliver Eberle (CoRR, 2025)